Number Systems for Deep Neural Network Architectures von Ghada Alsuhli | ISBN 9783031381355

Number Systems for Deep Neural Network Architectures

von Ghada Alsuhli und weiteren
Mitwirkende
Autor / AutorinGhada Alsuhli
Autor / AutorinVasilis Sakellariou
Autor / AutorinHani Saleh
Autor / AutorinMahmoud Al-Qutayri
Autor / AutorinBaker Mohammad
Autor / AutorinThanos Stouraitis
Buchcover Number Systems for Deep Neural Network Architectures | Ghada Alsuhli | EAN 9783031381355 | ISBN 3-031-38135-1 | ISBN 978-3-031-38135-5

Number Systems for Deep Neural Network Architectures

von Ghada Alsuhli und weiteren
Mitwirkende
Autor / AutorinGhada Alsuhli
Autor / AutorinVasilis Sakellariou
Autor / AutorinHani Saleh
Autor / AutorinMahmoud Al-Qutayri
Autor / AutorinBaker Mohammad
Autor / AutorinThanos Stouraitis

This book provides readers a comprehensive introduction to alternative number systems for more efficient representations of Deep Neural Network (DNN) data. Various number systems (conventional/unconventional) exploited for DNNs are discussed, including Floating Point (FP), Fixed Point (FXP), Logarithmic Number System (LNS), Residue Number System (RNS), Block Floating Point Number System (BFP), Dynamic Fixed-Point Number System (DFXP) and Posit Number System (PNS). The authors explore the impact of these number systems on the performance and hardware design of DNNs, highlighting the challenges associated with each number system and various solutions that are proposed for addressing them.